Developments toward a novel methodology for spent nuclear fuel verification

One of the tasks in nuclear safeguards is to regularly inspect spent nuclear fuel discharged from nuclear power reactors and verify the integrity of it, so that illegal removal and diversion of radioactive material can be promptly discovered. In the current project, which is a collaboration between Chalmers University of Technology and SCK CEN, a novel methodology for non-intrusive inspection of spent nuclear fuel is under development. The methodology consists of two main steps: 1) neutron flux and its gradient are measured inside spent nuclear fuel assemblies using small neutron detectors; and 2) the measurements are processed using an Artificial Neural Network (ANN) algorithm to identify the number and location of possible fuel pins that have been removed from the fuel assemblies and/or replaced with dummies. The use of small neutron detectors simplifies the inspection procedure since the fuel assemblies are not moved from their storage position. In addition, the neutron flux gradient measurements and its processing with the ANN algorithm have the potential for more detailed results. Different aspects have been investigated for the development of the methodology. For the first step of the methodology, the concept of a new neutron detector has been studied via Monte Carlo simulations and it relies on the use of optical fiber-mounted neutron scintillators. The outcome of the computational study shows that the selected detector design is a viable option since it has a suitable size to be introduced inside a fuel assembly and can measure neutron flux gradients. Then, experimental work has been carried out to test and characterize two optical fiber-based neutron scintillators that can be used to build the detector, with respect to detection of thermal neutrons and sensitivity to gamma radiation.For the second step of the methodology, a machine learning algorithm based on ANN is studied. At this initial stage, a simpler problem has been considered, i.e., an ANN has been prepared, trained and tested using a dataset of synthetic neutron flux measurements for the classification of PWR nuclear fuel assemblies according to the total amount of missing fuel, without including neutron flux gradient measurements and without localizing the anomalies. From the comparison with other machine learning methods such as decision trees and k-nearest neighbors, the ANN shows promising performance

Conceptual design and initial evaluation of a neutron flux gradient detector

Identification of the position of a localized neutron source, or that of local inhomogeneities in a multiplying or scattering medium (such as the presence of small, strong absorbers) is possible by measurement of the neutron flux in several spatial points, and applying an unfolding procedure. It was suggested earlier, and it was confirmed by both simulations and pilot measurements, that if, in addition to the usually measured scalar (angularly integrated) flux, the neutron current vector or its diffusion approximation (the flux gradient vector) is also considered, the efficiency and accuracy of the unfolding procedure is significantly enhanced. Therefore, in support of a recently started project, whose goal is to detect missing (replaced) fuel pins in a spent fuel assembly by non-intrusive methods, this idea is followed up. The development and use of a dedicated neutron detector for within-assembly measurements of the neutron scalar flux and its gradient are planned. The detector design is based on four small, fiber-mounted scintillation detector tips, arranged in a rectangular pattern. Such a detector is capable of measuring the two Cartesian components of the flux gradient vector in the horizontal plane. This paper presents an initial evaluation of the detector design, through Monte Carlo simulations in a hypothetical scenario

Identification of diversions in spent PWR fuel assemblies by PDET signatures using Artificial Neural Networks (ANNs)

Spent nuclear fuel represents the majority of materials placed under nuclear safeguards today and it requires to be inspected and verified regularly to promptly detect any illegal diversion. Research is ongoing both on the development of non-destructive assay instruments and methods for data analysis in order to enhance the verification accuracy and reduce the inspection time. In this paper, two models based on Artificial Neural Networks (ANNs) are studied to process measurements from the Partial Defect Tester (PDET) in spent fuel assemblies of Pressurized Water Reactors (PWRs), and thus to identify at different levels of detail whether nuclear fuel has been replaced with dummy pins or not. The first model provides an estimation of the percentage of replaced fuel pins within the inspected fuel assembly, while the second model determines the exact configuration of the replaced fuel pins. The two models are trained and tested using a dataset of Monte-Carlo simulated PDET responses for intact spent PWR fuel assemblies and a variety of hypothetical diversion scenarios. The first model classifies fuel assemblies according to the percentage of diverted fuel with a high accuracy (96.5%). The second model reconstructs the correct configuration for 57.5% of the fuel assemblies available in the dataset and still retrieves meaningful information of the diversion pattern in many of the misclassified cases

Ringhals Diagnostics and Monitoring, Annual Research Report 2021-2022

This report gives an account of the work performed by the Division of Subatomic, High Energy and Plasma Physics (formerly, Division of Nuclear Engineering), Chalmers, in the frame of a research collaboration with Ringhals, Vattenfall AB, contract No. 4501747546-003. The contract constitutes a one-year co-operative research work concerning diagnostics and monitoring of the PWR units. The work in the contract has been performed between 1 July 2021 and 30 June 2022